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Implementation of Low-cost Autonomous Car for Lane Recognition and Keeping based on Deep Neural Network model

  • Song, Mi-Hwa (School of Information and Communication Science, Semyung University)
  • Received : 2021.01.07
  • Accepted : 2021.01.15
  • Published : 2021.02.28

Abstract

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

Keywords

References

  1. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
  2. Glorot, X., & Bengio, Y. (2010, March). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249-256). JMLR Workshop and Conference Proceedings.
  3. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
  4. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3), 211-252. https://doi.org/10.1007/s11263-015-0816-y
  5. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
  6. Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. Vol. 1. No. 1. Cambridge: MIT Press, 1998
  7. Baird, Leemon. "Residual algorithms: Reinforcement learning with function approximation." Proceedings of the twelfth international conference on machine learning. 1995.
  8. Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018, April). Deep reinforcement learning that matters. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
  9. Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34(6), 26-38. https://doi.org/10.1109/MSP.2017.2743240
  10. Reinforcement learning - Wikipedia https://en.wikipedia.org/wiki/Reinforcement_learning
  11. Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 37(3), 362-386. https://doi.org/10.1002/rob.21918
  12. Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2016). End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research, 17(1), 1334-1373.
  13. Pomerleau, D. A. (1989). Alvinn: An autonomous land vehicle in a neural network. CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND PSYCHOLOGY PROJECT.
  14. Lecun, Y., Cosatto, E., Ben, J., Muller, U., & Flepp, B. (2004). Dave: Autonomous off-road vehicle control using end-to-end learning. DARPA-IPTO Final Report.
  15. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., ... & Zieba, K. (2016). End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.
  16. NVIDIA. GPU-Based Deep Learning Inference : A Performance and Power Analysis. Technical Report November, 2015.